•Potential enablers to blockchain technology (BT) adoption in Indian Agriculture Supply Chain (ASC) are identified.•Relationships among the enablers and their inter-dependencies established using ...combined ISM and DEMATEL methodology.•Traceability of the agricultural products was found to be the key enabler for adoption of BT in ASC.•Other enablers include auditability, immutability, and provenance.•This study is first of its kind on adoption of BT in Indian ASC.
Blockchain Technology (BT) has led to a disruption in the supply chain by removing the trust related issues. Studies are being conducted worldwide to leverage the benefits provided by BT in improving the performance of the supply chains. The literature reveals BT to offer various benefits leading to improvements in the sustainable performance of the agriculture supply chains (ASC). It is expected that BT will bring a paradigm shift in the way the transactions are carried in the ASC by reducing the high number of intermediaries, delayed payments and high transaction lead times. India, a developing economy, caters to the food security needs of an ever-growing population and faces many challenges affecting ASC sustainability. It is therefore essential to adopt BT in the ASC to leverage the various benefits. In this study, we identify and establish the relationships between the enablers of BT adoption in ASC. Thirteen enablers were identified from the literature and validated by the experts before applying a combined Interpretive Structural Modelling (ISM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology to envision the complex causal relationships between the identified BT enablers. The findings from the study suggest that, among the identified enablers, traceability was the most significant reason for BT implementation in ASC followed by auditability, immutability, and provenance. The findings of the study will help the practitioners to design the strategies for BT implementation in agriculture, creating a real-time data-driven ASC. The results will also help the policymakers in developing policies for faster implementation of BT ensuring food safety and sustainable ASCs.
•Potential adoption barriers of Industry 4.0 are identified from the literature.•Industry experts validated the identified barriers and developed contextual relationships between them.•Relationships ...established among the barriers using ISM and Fuzzy MICMAC analysis.•Driving and dependence power of barriers in Indian manufacturing industry context is developed.•The results reveal the direct and indirect effects of identified barriers in the Industry 4.0 adoption.
The aim of this paper is the analysis of potential barriers which would hinder the manufacturing organizations from embracing Industry 4.0. This paper establishes relationships among the barriers using interpretive structural modeling (ISM) and finds out driving and dependence power of barriers, using fuzzy MICMAC (Matriced’ Impacts Croise´s Multiplication Applique´e a´ un Classement) analysis. A group of experts from industry and academia was consulted and ISM methodology was used to develop the contextual relationship among the identified barriers to Industry 4.0 adoption. The results of ISM were used as input to fuzzy MICMAC analysis for determining the driving and dependence power of the Industry 4.0 adoption barriers. The results are helpful in identifying and classifying the significant barriers, revealing the direct and indirect effects of each identified barrier on the Industry 4.0 adoption. The findings will help the practitioners and the policymakers for a detailed understanding of the Industry 4.0 adoption process and the barriers hindering its implementation. They may utilize the framework and the driving-dependence power of the barriers to understanding the inter-relationship between the barriers to building a valid and operative digital manufacturing platform. The study is first of its kind to identify industry 4.0 adoption barriers and develop hierarchical relationships between them using ISM and fuzzy MICMAC methodology in the Indian manufacturing context.
The lack of industrialization, inadequacy of the management, information inaccuracy, and inefficient supply chains are the significant issues in an agri-food supply chain. The proposed solutions to ...overcome these challenges should not only consider the way the food is produced but also take care of societal, environmental and economic concerns. There has been increasing use of emerging technologies in the agriculture supply chains. The internet of things, the blockchain, and big data technologies are potential enablers of sustainable agriculture supply chains. These technologies are driving the agricultural supply chain towards a digital supply chain environment that is data-driven. Realizing the significance of a data-driven sustainable agriculture supply chain we extracted and reviewed 84 academic journals from 2000 to 2017. The primary purpose of the review was to understand the level of analytics used (descriptive, predictive and prescriptive), sustainable agriculture supply chain objectives attained (social, environmental and economic), the supply chain processes from where the data is collected, and the supply chain resources deployed for the same. Based on the results of the review, we propose an application framework for the practitioners involved in the agri-food supply chain that identifies the supply chain visibility and supply chain resources as the main driving force for developing data analytics capability and achieving the sustainable performance. The framework will guide the practitioners to plan their investments to build a robust data-driven agri-food supply chain. Finally, we outline the future research directions and limitations of our study.
•Machine learning techniques enhance data-driven decision-making in agricultural supply chains.•Systematic literature review based on 93 papers on machine learning applications in agricultural supply ...chains.•Machine learning applications supports in developing sustainable agriculture supply chains.•A machine learning applications framework is proposed and related future perspectives are presented.
Agriculture plays an important role in sustaining all human activities. Major challenges such as overpopulation, competition for resources poses a threat to the food security of the planet. In order to tackle the ever-increasing complex problems in agricultural production systems, advancements in smart farming and precision agriculture offers important tools to address agricultural sustainability challenges. Data analytics hold the key to ensure future food security, food safety, and ecological sustainability. Disruptive information and communication technologies such as machine learning, big data analytics, cloud computing, and blockchain can address several problems such as productivity and yield improvement, water conservation, ensuring soil and plant health, and enhance environmental stewardship. The current study presents a systematic review of machine learning (ML) applications in agricultural supply chains (ASCs). Ninety three research papers were reviewed based on the applications of different ML algorithms in different phases of the ASCs. The study highlights how ASCs can benefit from ML techniques and lead to ASC sustainability. Based on the study findings an ML applications framework for sustainable ASC is proposed. The framework identifies the role of ML algorithms in providing real-time analytic insights for pro-active data-driven decision-making in the ASCs and provides the researchers, practitioners, and policymakers with guidelines on the successful management of ASCs for improved agricultural productivity and sustainability.
Performance measures and metrics (PMM) is identified to be an essential aspect of managing diverse supply chains. The PMM improves the firm's performance by providing open and transparent ...communication between the various stakeholders of an organisation. The literature suggests that big data analytics has a positive impact on the supply chain and firm performance. Presently, the literature lack studies that recognise the PMM relevant to big data-driven supply chain (BDDSC). The present study is based on a comprehensive review of 66 papers published with the primary objective to identify the various PMMs used to evaluate the BDDSC. The findings suggest that the PMMs applicable to BDDSC can be classified into two non-mutually exclusive categories. The first category represents 24 performance measures used to evaluate the performance of the big data analytics capability and the second category represents 130 measures used for assessing the performance of BDDSC processes. The study also reports the emergence of new performance measures based on increasing use of predictive and social analytics in BDDSC. Based on the results of the study a framework on BDDSC performance measurement system is proposed which will guide the managers to have a robust performance measurement system in their organisation.
Supply chain resilience (SCRes) and performance have become increasingly important in the wake of the recent supply chain disruptions caused by subsequent pandemics and crisis. Besides, the context ...of digitalization, integration, and globalization of the supply chain has raised an increasing awareness of advanced information processing techniques such as Artificial Intelligence (AI) in building SCRes and improving supply chain performance (SCP). The present study investigates the direct and indirect effects of AI, SCRes, and SCP under a context of dynamism and uncertainty of the supply chain. In doing so, we have conceptualized the use of AI in the supply chain on the organizational information processing theory (OIPT). The developed framework was evaluated using a structural equation modeling (SEM) approach. Survey data was collected from 279 firms representing different sizes, operating in various sectors, and countries. Our findings suggest that while AI has a direct impact on SCP in the short-term, it is recommended to exploit its information processing capabilities to build SCRes for long-lasting SCP. This study is among the first to provide empirical evidence on maximizing the benefits of AI capabilities to generate sustained SCP. The study could be further extended using a longitudinal investigation to explore more facets of the phenomenon.
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•Systematic literature review (SLR) of 85 papers on Industry 4.0.•The selected papers were classified into five research categories.•A sustainable Industry 4.0 framework is ...proposed.•Future research perspectives are presented.•Significant research contributions and implications are presented.
Industry 4.0 and its other synonyms like Smart Manufacturing, Smart Production or Internet of Things, have been identified as major contributors in the context of digital and automated manufacturing environment. The term industry 4.0 comprises a variety of technologies to enable the development of the value chain resulting in reduced manufacturing lead times, and improved product quality and organizational performance. Industry 4.0 has attracted much attention in the recent literature, however there are very few systematic and extensive review of research that captures the dynamic nature of this topic. The rapidly growing interest from both academics and practitioners in Industry 4.0 has urged the need for review of up-to-date research and development to develop a new agenda. Selected 85 papers were classified in five research categories namely conceptual papers on Industry 4.0, human-machine interactions, machine-equipment interactions, technologies of Industry 4.0 and sustainability. The review primarily attempted to seek answers to the following two questions: (1) What are different research approaches used to study Industry 4.0? and (2) What is the current status of research in the domains of Industry 4.0?. We propose a sustainable Industry 4.0 framework based on the findings of the review with three critical components viz., Industry 4.0 technologies, process integration and sustainable outcomes. Finally, the scope of future research is discussed in detail.
The smart manufacturing systems (SMS) offer several advantages compared to the traditional manufacturing systems and are increasingly being adopted by manufacturing organizations as a strategy to ...improve their performance. Developing an SMS is expensive and complicated, integrating together various technologies such as automation, data exchanges, cyber-physical systems (CPS), artificial intelligence, internet of things (IoT), and semi-autonomous industrial systems. The Small, Medium and Micro Enterprises (SMMEs) have limited resources and therefore, would like to see the benefits from investments before allowing adopting SMS. This study uses a combination of exploratory and empirical research design to identify and validate the performance measures relevant to the evaluation of SMS investments in auto-component manufacturing SMMEs based in India. The study found that an Industry 4.0 enabled SMS offer more competitive benefits compared to a traditional manufacturing system. The planned investments in SMS can be evaluated on ten performance dimensions namely, cost, quality, flexibility, time, integration, optimized productivity, real-time diagnosis & prognosis, computing, social and ecological sustainability. Proposed novel Smart Manufacturing Performance Measurement System (SMPMS) framework is expected to guide the practitioners in SMMEs to evaluate their SMS investments.
The study investigates the relationship between the information and communication-enabled supply chain integration (SCI) and sustainable supply chain performance (SSCP). Moreover, to the best of our ...knowledge, there is no empirical evidence on the impact of blockchain technologies (BT) on the SSCP. Therefore, the primary aim of this study is to assess the relationship between BT and SSCP. More specifically, the study was conducted to examine the direct influence of BT on SCI and SSCP and the interactive effect of BT and SCI on SSCP. Based on the dynamic capability theoretical lens, the present study conceptualizes the use of BT as a specific IT resource to collaborate and reconfigure the ties with the upstream and downstream supply chain members to achieve SSCP. The results of the study support the hypothesis stating that BT positively influences the SSCP. The results recognize the role of SCI as a significant mediating variable between the BT and SSCP. The result indicates the strong influence of SCI with full mediation effect on the relationship between the BT and SSCP.
•A blockchain adoption (BA) model based psychological constructs.•Model validation using structural equation modelling.•Significant psychological constructs identified.•Prediction model for BA ...developed applying bayesian network analysis.•The prediction model will help to predict readiness of organisations for BA.
The purpose of this paper is to provide a decision support system for managers to predict an organization's probability of successful blockchain adoption using a machine learning technique. The study conceptualizes blockchain technology as a dynamic capability that should be possessed by the organization to remain competitive. The factors influencing the blockchain adoption behavior were modeled using the theoretical lens of the Technology Acceptance Model and Technology-organisation-Environment framework. The findings identify competitor pressure, partner readiness, perceived usefulness, and perceived ease of use as the most influencing factors for blockchain adoption. A predictive decision support system was developed using a Bayesian network analysis featuring the significant factors that can be used by the decision-makers for predicting the probability of blockchain adoption in their organization. The prior probability values reported in the study may be used as indicators by the practitioners to predict their blockchain adoption probability. The practitioner will be required to substitute these probability values (high or low), as applicable to their organization to estimate the adoption probability. The use of the decision support system is likely to help the decision-makers to assess their adoption probability and develop future adoption strategies.
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